JailBench introduces the first comprehensive security assessment benchmark specifically designed to evaluate Chinese LLMs against sophisticated jailbreak attacks.

• Employs diverse attack strategies including role-playing, context manipulation, encoding tricks, and instruction following vulnerabilities
• Evaluates models across multiple risk categories including illegal content, discrimination, privacy violations, and harmful information
• Reveals significant security gaps in current Chinese LLMs, with open-source models showing higher vulnerability than proprietary ones
• Demonstrates that translation-based testing often fails to detect vulnerabilities specific to Chinese linguistic features

This research is crucial for developing more secure AI systems in Chinese markets by identifying and addressing language-specific security vulnerabilities before deployment.

JailBench: A Comprehensive Chinese Security Assessment Benchmark for Large Language Models